Approximate Inference

Dr Kaspar Märtens, The Alan Turing Institute

Approximate inference techniques are vital to the production of scalable algorithms that can operate on large high-dimensional data sets. This lecture will discuss two frequently used inference methods in Bayesian machine learning: variational inference and stochastic gradient descent with case studies.

Topics covered include:

• Variational inference
• Stochastic gradient descent